In this paper, a gain-scheduling, robust, and shared controller is proposed to assist drivers in tracking vehicle reference trajectory. In the controller, the driver steering parameters such as delay time, preview time, and steering gain are assumed to be varying with respect to the different characteristics of drivers, vehicle states, and driving scenarios. Meanwhile, the modeling errors and uncertainties in the tire cornering stiffness are also considered in the driver–vehicle system model and the controller design. A global objective function, considering the tracking error, the driver's physical and mental workloads, and the control effort, is designed to optimize the overall performance of the driver–vehicle system. Constraint on eigenvalue placement is added to the controller design to improve the performance of the closed-loop driver–vehicle system. Simulation results under different maneuvers show that the controller can significantly improve the system performance and reduce the driver's workloads. The controller can reduce the delay time of the driver–vehicle system in emergency maneuvers, particularly for inexperienced drivers.

A Gain-Scheduling Driver Assistance Trajectory-Following Algorithm Considering Different Driver Steering Characteristics

Output-feedback robust control for vehicle path tracking considering different human drivers’ characteristics

Information Management of Demand-responsive Mobility Service Based on Autonomous Vehicles

Most of the existing torque vectoring control (TVC) strategies are “vehicle-centered,” hence, fail to consider how driving characteristics affect the vehicle's motion. To address this issue, we propose a hierarchically structured human- centered TVC (HCTVC) for distributed drive electric vehicles (EVs). To do this we design the experiment to characterize the drivers’ behavior with different skills. Then a global objective function is formulated considering the driver's physical and mental workload, as well as the agility and stability of the vehicle, to optimize the overall performance of the driver-vehicle system. Next, an adaptive second-order sliding mode controller (ASOSM) based on the backstepping method is designed as the upper-level controller to improve system robustness. Finally, a multi-objective optimization algorithm based on the weight self-tuning strategy is developed as the lower-level controller to solve the torque distribution problem. We further obtain the analytical solution to this optimization problem. Experimental results on the asphalt and icy roads confirm that the proposed HCTVC significantly enhances the vehicle transient response and reduces the driver's workload.

Human-Centered Torque Vectoring Control for Distributed Drive Electric Vehicle Considering Driving Characteristics

Vehicle group situation is the status and situation of dynamic permutation which is composed of target vehicle and neighboring traffic entities. It is a concept which is frequently involved in the research of traffic flow theory, especially the active vehicle security. Studying vehicle group situation in depth is of great significance for traffic safety. Three-lane condition was taken as an example; the characteristics of target vehicle and its neighboring vehicles were synthetically considered to restructure the vehicle group situation in this paper. The Gamma distribution theory was used to identify the vehicle group situation when target vehicle arrived at the end of the study area. From the perspective of driver-vehicle feature evolution, the reverse reconstruction method of vehicle group situation in the urban road network was proposed. Results of actual driving, virtual driving, and simulation experiments showed that the model established in this paper was reasonable and feasible.

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Study on Reverse Reconstruction Method of Vehicle Group Situation in Urban Road Network Based on Driver-Vehicle Feature Evolution

Development of a Prediction Method for Driver's Propensity☆

The study of route-choice behavior has been an important part of the study of urban-road transport system. In previous studies, the emergencies of the road network have been studied mostly from different angles under random dynamic conditions. But the condition of route-choice behavior of unexpected-destination trip was rarely mentioned. So in this paper, “unexpected radiation field” is put forward, at the same time, the characteristic of it is also studied. On the basis of this, route-choice behavior of unexpected-destination trip under random dynamic conditions is researched with a combination of “radiation field method” and discrete dynamic Bayesian network. Studies display that the location and important of the unexpected destination affect the route choice behavior closely. The simulation results show that, the model had a positive meaning, and enabled to simulate and reproduce the decision-making process more appropriately.

Jinglei Zhang

Papers

Development of a Prediction Method for Driver's Propensity☆

Research on Route-Choice Behavior of Unexpected-Destination Trip Under Random Dynamic Conditions